one noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 39"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8068  -1.0493   0.5770   0.9415   2.5190  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.18447    0.05074   3.635 0.000277 ***
## n1           2.20269    0.13545  16.262  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2256.7  on 1998  degrees of freedom
## AIC: 2260.7
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"

## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"

## [1] "effects model, sigma= 39"
## [1] "one noise variable, logistic regression effects model, sigma= 39 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.213  -1.203   1.142   1.152   1.328  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.06311    0.04938   1.278  0.20121   
## n1           0.03592    0.01229   2.922  0.00347 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2764.0  on 1998  degrees of freedom
## AIC: 2768
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one noise variable, logistic regression Laplace noised 39 train mean deviance 1.99379376005558"

## [1] "one noise variable, logistic regression Laplace noised 39 test mean deviance 2.00462219580913"

## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3619  -1.1570   0.9662   1.1980   1.2169  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.04838    0.04731  -1.023  0.30650   
## n1          -0.06366    0.01954  -3.258  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2761.8  on 1998  degrees of freedom
## AIC: 2765.8
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"

## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"

## [1] "********"
## [1] "one noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   2.000   2.000   2.000   2.001   2.003 
## [1] 0.0006789706
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   2.001   2.002   2.003   2.003   2.017 
## [1] 0.004185064
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.698   3.949   4.025   4.051   4.104   4.494 
## [1] 0.2062731
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   2.000   2.000   2.002   2.002   2.011 
## [1] 0.00283558
## [1] "********"
## [1] "*************************************************************"

one variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 0"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1243  -1.1809   0.4704   1.1554   1.5778  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.4731     0.0542    8.73   <2e-16 ***
## x1            3.1777     0.2114   15.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2434.7  on 1998  degrees of freedom
## AIC: 2438.7
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"

## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"

## [1] "effects model, sigma= 0"
## [1] "one variable, logistic regression effects model, sigma= 0 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1243  -1.1809   0.4704   1.1554   1.5778  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.4731     0.0542    8.73   <2e-16 ***
## x1            3.1777     0.2114   15.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2434.7  on 1998  degrees of freedom
## AIC: 2438.7
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression Laplace noised 0 train mean deviance 1.75629049009229"

## [1] "one variable, logistic regression Laplace noised 0 test mean deviance 1.74484448505444"

## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0811  -1.1892   0.4966   1.1600   1.5642  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.45308    0.05326   8.508   <2e-16 ***
## x1           2.99703    0.20478  14.636   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2460.2  on 1998  degrees of freedom
## AIC: 2464.2
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"

## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"

## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.754   1.763   1.769   1.771   1.776   1.794 
## [1] 0.01072632
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.755   1.763   1.770   1.771   1.775   1.792 
## [1] 0.01027413
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.754   1.763   1.769   1.771   1.776   1.793 
## [1] 0.01073833
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.754   1.762   1.771   1.771   1.777   1.793 
## [1] 0.01184963
## [1] "********"
## [1] "*************************************************************"

one variable plus noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 6"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5658  -0.9120   0.3055   0.8035   2.7112  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.68760    0.06161   11.16   <2e-16 ***
## x1           3.18452    0.23641   13.47   <2e-16 ***
## n1           2.45247    0.15572   15.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 1990.5  on 1997  degrees of freedom
## AIC: 1996.5
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"

## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"

## [1] "effects model, sigma= 6"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 6 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0433  -1.1982   0.5191   1.0880   1.7140  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.49382    0.05753   8.583   <2e-16 ***
## x1           3.07418    0.20523  14.979   <2e-16 ***
## n1           0.02769    0.01552   1.784   0.0743 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2449.3  on 1997  degrees of freedom
## AIC: 2455.3
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one variable plus noise variable, logistic regression Laplace noised 6 train mean deviance 1.76678082468646"

## [1] "one variable plus noise variable, logistic regression Laplace noised 6 test mean deviance 1.78498006179382"

## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2012  -1.1757   0.5026   1.1657   1.5936  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.42346    0.05493   7.710 1.26e-14 ***
## x1           3.00699    0.20534  14.644  < 2e-16 ***
## n1          -0.05278    0.02435  -2.167   0.0302 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2455.4  on 1997  degrees of freedom
## AIC: 2461.4
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"

## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"

## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.732   1.762   1.768   1.769   1.776   1.792 
## [1] 0.01311197
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.731   1.763   1.767   1.768   1.779   1.787 
## [1] 0.01346241
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.315   3.456   3.589   3.610   3.751   4.039 
## [1] 0.2108874
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.759   1.771   1.793   1.796   1.809   1.876 
## [1] 0.03186603
## [1] "********"
## [1] "*************************************************************"